In the real world, your application is running in a container among hundreds or thousands of other containers. The system's resources are also probably being managed by a hypervisor. The CPU is shared among N tenants _and_ is overcommitted. It's not that much to ask to optimize where you can, when it isn't unreasonably difficult.
More importantly, this attitude is precisely why software sucks today. "[CPU, Memory, Disk] is cheap, engineering time isn't." Fuck that. Bad engineering time is _incredibly_ expensive. This is an excuse to not spend time learning the ins and outs of your language, and your hardware.
It also frustrates me to no end that people are so deeply incurious. This seems to only happen in tech, which is baffling considering how incredibly broad and deep the industry is. Instead, everyone clamors to learn the new Javascript abstraction that lets them get even further away from the reality of what the magic boxes are doing.
> It also frustrates me to no end that people are so deeply incurious. This seems to only happen in tech
This doesn't match my observations. In many fields, training is limited and hides the details. We train workers to repeat specific tasks and they excel at them. But they don't have conceptual understanding. Any explanation of why things are done the way they are is surface-level. You can see it when a procedure fails for some reason. They way to deal with is to 1) do basic checks 2) try again and if it still fails 3) delegate to someone else. Nobody is going to troubleshoot or optimize tasks unless that's their main job.
It happens in construction, in the kitchens, on the assembly lines, and in the offices. It happens because it gets the job done.
There are a lot of developers who learn how to do a task and never question whether the task could be automated, either away or to reduce errors. They just do the same mundane task four hours a week in perpetuity.
That’s the part that frustrates me. Your job is to automate things. So why aren’t you automating things?
You're right. My wording was flat-out wrong, and I apologize. A more accurate sentiment (IMO) would be "that this happens in tech is baffling, considering..."
Yes, but in most fields, conceptual understanding doesn't matter for most tasks.
That's the problem with software. A brick-layer doesn't need to understand structural stability, but in software every task is structure validation, and people expect brick-layers to be productive.
> A brick-layer doesn't need to understand structural stability
Maybe a “junior” brick layer doesn’t need to, as much as a junior engineer doesn’t need to understand the ins and outs of their language. But a senior brick layer, or an architect, needs to understand more of the details so that they can set out the plan for the junior.
Some apple-pickers think the point of picking apples is to prove how good you are at climbing trees. They'll never not be mad at pluckers who just pluck the nearest apple, and the people who reward them for the apples, and the world that doesn't understand the pluckers picked their apples wrong, and didn't even earn them, and they're not even real apples.
A farmer who understands the value of crop rotation is completely right to be upset when other farmers are monocropping and killing the soil, or when they squander the local aquifer. It's directly impacting the sustainability of their industry.
Somehow, the trees keep getting exponentially bigger, and yet the pies we’re getting are actually kinda just getting smaller and worse. Maybe just picking the nearest apples isn’t working out as well as they say it is.
Climbing a ladder takes more time, so in order to get the apples to market faster, the apple tree owners keep the pickers focused on only picking the apples within arm's reach.
The owners also disallow the use of ladders because the pool of candidates to hire remains bigger.
It's one thing if you know the optimal (or at least approaching it) solution, and deliberately use something else for other reasons. At least thought went into it.
I'm not upset at this simply for purity's sake; it directly impacts me in my day-to-day job. A hundred devs individually decide that good enough is good enough (deliberately or otherwise), and suddenly my infrastructure is dealing with traffic and query patterns it shouldn't have to, and then I have to explain what network bandwidth limits are, and why it's absurd that we hit them in the first place.
In general, what I'm railing against is the continued push towards simplification and abstraction, while not requiring the understanding of what lies beneath. The hyperscalers certainly aren't trying to foster that attitude in-house – someone has to build the abstractions in the first place, after all, and keep datacenters humming. Yet they're happily shoveling it out, because it's a win-win for them: fewer people have the skills necessary to compete (or even to simply not use their services), and more people are able to use their services.
It's like the contractor (or subcontractor) who built the house you live in. They don't have to pay your monthly heating or cooling bills, so they often do whatever is fastest and cheapest for them.
Who cares if they didn't insulate a wall correctly or put an inefficient system in? If they can get it by the inspector, they will do it. If they can save themselves $100, that is all that matters to them. Who cares if it adds $10 to each month's utility bills for the next 30 years? They got their money and are now long gone.
Most people don't care about their job, they just want to put in minimum viable effort, get paid and go home to their family. Typically they do this because putting more effort didn't bring them anything (not even some recognition of efforts) or even backfired. It's applicable to all jobs, not only in tech.
I get that. My comment has nothing to do with people's pride in their work. I was commenting that there's not enough user demand for better preforming software because users don't seem to mind the software hellscape we currently live in.
Agree with the sentiment. However it's hard to stay curious, even harder to stay up-to-date.
I liked fiddling with storage for a while, got really into it, deepened my knowledge about it. A couple years later I realized everything else (networking, architectures, languages) developed so much, mot of my (non-basic) knowledge was obsolete. Picking up where I left off with all technologies is incredibly hard and caused fatigue.
Now I'm at a point where I have the feeling I don't know nothing about anything. It's factually not true, but my gut feeling tells this. Would I be younger, this would trigger a lot of anxiety. Thankfully I can janfle this by now.
That's understandable. I'm into databases (both professionally and personally), so I get to touch a wide variety of things. Following a query the whole way is pretty neat. Typed query --> client --> wire protocol --> network --> server --> DB frontend --> DB planner --> DB storage engine --> OS --> Memory / Disk. `blktrace` is super interesting to watch commands being sent to the disk.
When you are deeply involved in tech both personally and professionally you are probably very passionate about this and makes sense that you'd only look closely at this field and think "people are so deeply incurious. This seems to only happen in tech".
Tech is also one of the (if not the) most dynamic and fast evolving field a normal person will ever touch. Curiosity in tech can drain every single bit of free time and energy you have and you will hardly keep up with the progress, maybe barely scratch the surface. But people's available free time and energy wanes and curiosity is a collateral victim.
I've painfully gone through the entire cycle of this, including the bit of resurgence later on when you have a combination of free time but less energy. What I can say is that this absolutely does not happen just in tech. If anything tech is flooded with people with more curiosity than almost any other field.
> When you are deeply involved in tech both personally and professionally you are probably very passionate about this and makes sense that you'd only look closely at this field and think "people are so deeply incurious. This seems to only happen in tech".
Good point. I commented in a sibling post to the same effect.
I've certainly felt the personal strain of time sink and procrastination in my homelab. It's currently running k3os, which has been dead for about four years now, because everything I want is still running, and I never seem have the motivation on the weekend to yell at my laptop when I could be playing board games.
> including the bit of resurgence later on when you have a combination of free time but less energy.
I'm guessing that will happen in another decade or so, when my kids are grown.
Life is very different for many people and I think we just need to build empathy for people who treat a job as just a job. If they deliver and are not unkind about it there's nothing wrong about not going above and beyond the bare minimum, which is what they are paid for.
As I commented above, a large part of my umbrage stems from the impact these decisions have on my job. I dislike having to work harder because others didn't want to go above the bare minimum.
This isn't unique to any one company, nor my personal experience. At my last job, my team was initially human triaging practically all alerts, because we had the rare ability to read logs. I spoke to someone at Slack once who was stuck doing the same. That's an absurdly low expected level of competence.
You don't _have_ to work harder, as evidenced by those people who do the bare minimum. You just care about your work more than people who pay you for it (or the manager hired to manage you), which is the cause of your frustration here IMO.
I care about my teammates. Letting them down by not working to my full potential makes me feel bad. I think this is embedded into my pysche from years of military experience and then shift work.
> people are so deeply incurious. This seems to only happen in tech
It happens everywhere. If anything, techies are more curious than the average Joe. How many fellow programmers can you nerd-snipe with a comment that makes them say, “Well, that’s strange…”
I can somewhat echo some of the statements here and provide my own experience that is similar.
I spend a decent amount of time writing decent C++ code. My friends in other parts of the industry are writing certainly better C++ code than me because they are doing it in environments that are more constricted in various ways. In either case, I do spend my time catching up a bit and would consider myself a competent C++21 programmer in some ways.
My experience and my conversations lead me to understand there is so much left on the table with even the most basic implementations. When I implement it correctly in C++ we get close to some of the theoretical limits for the hardware for some workloads, compared to something that is literally 1% as fast running in NodeJS.
Wit that said, for so many situations I cannot justify the time and complexity to use C++ for many projects. At least for the stage most projects are in. In theory this optimization can happen later, but it never really does because the horizontal (or sometimes even vertical) scaling kicks in and we're all just willing to throw a few more dollars at the problem instead of re-engineering it. Sure, some of the really big companies like Netflix find a decent reason from time to time to invest the engineering time squeeze out those numbers, but it's becoming the exception and not the rule.
I've also found that not having some optimization mindset from the get go limits your product to achieve only a local maxima of performance in the end. It might not even be a good local maxima.
It's best to have at least some optimization considerations from the start. I'm saying some because too much is a problem.
> In the real world, your application is running in a container among hundreds or thousands of other containers
I mean, that’s an engineering decision too. In my day job we’re capturing, pre-processing, running inference on, and post-processing about 500Mpx/s worth of live image data at about 80ms/frame end-to-end at the edge. The processor SoM costs about $3000/unit and uses about 50W running flat out. The retail cost of our overall product is two orders of magnitude more than what the processor is worth but it incurs zero recurring costs for us.
Edit: and it’s got 64GB of Unified RAM that I’ve got all to myself :)
I was wondering if someone from a different sub-industry would disagree here :D
That sounds like a very interesting job, with quite different requirements and constraints from what I'm used to. One day, I'll get a job where application latency is critical, and optimizations matter deeply. Undoubtedly I'll find something else to be upset about, but at least it'll be a new complaint.
It’s hard to learn the ins and outs of dozens of programming languages. One doesn’t usually just use one or two PL over their entire career. I have worked professionally with at least PHP, Java, Python, JS, Go, and Ruby. That without taking into account the respective libraries and frameworks (and without taking into account as well the myriad of other components like dbs, web servers, caches, etc.)
It sounds like an excuse, I know. The true is I just don’t have that much time.
> In the real world, your application is running in a container among hundreds or thousands of other containers. The system's resources are also probably being managed by a hypervisor. The CPU is shared among N tenants _and_ is overcommitted. It's not that much to ask to optimize where you can, when it isn't unreasonably difficult.
And the real irony is that those programmers not giving a crap about performances and justifying it are resting on the shoulders of giants who went out of their way to have these kernels, hypervisors, containers, passthroughs, memory usage, etc. be as performant as they can.
Same for security.
Then you get the "I'm not in the business of writing fast code" and "I'm not a security company, I'm selling X and Y".
But they all, on a daily basis, use actual proper software written by people who know about these things.
In the real world, your application is running in a container among hundreds or thousands of other containers. The system's resources are also probably being managed by a hypervisor. The CPU is shared among N tenants _and_ is overcommitted.
When I read this, I thought the rest of your post would go entirely differently. As in, I immediately agreed with you, only for you to "turn this 180" (according to how I think about this at least :) )
It's not that much to ask to optimize where you can, when it isn't unreasonably difficult.
You take the above to mean we should optimize such that L2 cache is used as per the post as much as possible. Optimize the wazoo out of things.
But how does that even help in any way, when the CPU this runs on is like you said shared among N tenants and your carefully optimized L2 cache access is still going to be miss because another tenant got "your" CPU in between?
If you're paying for bare metal i.e. have the whole instance for yourself, by all means, if your domain actually requires you to use the system in such a way (things like high frequency trading come to mind), then optimize like that!
If you're running on seventeen levels of hypervisors that destroy any careful optimization in a random fashion anyway, then what's the point even? (non rhetorical question!)
It's a version of the tragedy of the commons. Yes, your software bloat isn't the main factor keeping you slow, and cleaning it up might be barely perceptible.
But... all the other software makes the same decision. Then, suddenly, everything on the computer is running at 10% efficiency.
In a complex, multitasking environment, keeping your code clean benefits other tasks as much or more than your own. It should be considered a responsibility.
I would agree with the sentiment but not necessarily the conclusion.
Like, don't use (the equivalent of) Stooge Sort (for your particular situation).
But unless you are in a very particular situation, it should be OK for everyone to "just use your language's built-in sort function" (hopefully that's a thing and you don't use something where you need to roll your own even in 2024) assuming that it uses a sane default like quicksort or merge sort that will work perfectly fine for most regular common situations.
Another example might be to not stupidly build slow algorithms saying "this won't see much data anyhow" (yes, I've seen that in PRs unfortunately) when a very simple hash lookup will ensure it's fast all the time. But it should be totally fine to assume that your language's hash table implementation is fine for common situations and you don't need to optimize anything unless you're a very special case.
> "[CPU, Memory, Disk] is cheap, engineering time isn't." Fuck that.
It is. It's absurdly cheap. I ensure I check the amount of time it would take for me to make a performance improvement against the runtime costs of my functions. It's rarely worth the extra effort.
Seriously, until you get into the millions of records per second level, you're almost never benefited. You may make your function 2x faster, at a cost of additional complexity, but you never run it enough in a year for it to pay itself back.
> Bad engineering time is _incredibly_ expensive.
Engineering time is expensive. Period. It speaks to the need to minimize it.
> This is an excuse to not spend time learning the ins and outs of your language, and your hardware.
All of which will change in a few years, which is fine, if you're also committing to keeping _all that code_ up to date right along with it. Otherwise you end up with an obscure mess that you have to unwind 5 years of context to understand and fix again.
Complexity and available mental contexts are forgotten costs. If your language even has that many "ins and outs" to begin with you may want to reconsider that.
> You may make your function 2x faster, at a cost of additional complexity, but you never run it enough in a year for it to pay itself back.
"You" is both singular and plural, which is often the problem with this thinking.
Is it worth spending a month of engineering time to make a page load in 50ms instead of 2s? Seems like a lot of engineering time for a noticeable but somewhat minor improvement.
But now, what if you have a million users who do this operation 100x/day? Absolutely worth it!
For example, I sure wish atlassian would spend a tiny bit of effort into making jira faster. Even if it is 1 second per ticket, since I'm viewing 100+ tickets per day that adds up. And there's many hundreds of us at the company doing the same thing, it really adds up.
Nit:
50ms vs 2000ms is 40x speed increase, i.e. ~1.5 order of magnitude.
I still keep words of my database optimization lecturer who said that by his experience optimization below 1 OOM aren’t worth it and most „good ones” are 3+
> Absolutely worth it!
Long reaching assumption. Even the biggest companies have limited resources (even if vast). Would you rather improve load times by 2x (from 500ms to 250ms) or improve checkout reliability from 99% to 99.5%? And there is much more to consider on some levels (e.g. planning for thermal efficiency is fun).
> I still keep words of my database optimization lecturer who said that by his experience optimization below 1 OOM aren’t worth it and most „good ones” are 3+
Like everything, it depends. Is the query gating a lot of other things, especially things that can be done in parallel? Shaving 10 ms off might very well be meaningful. Is it a large OLAP query, and the owning team has SLAs that depend on it? Going from 60 --> 55 minutes might actually matter.
The two biggest performance-related issues with RDBMS that I deal with, aside from indexing choices, are over-selection (why on earth do ORMs default to SELECT * ?), and poor JOIN strategies. Admittedly the latter is often a result of poor schema design, but for example, the existence of semi/anti-joins seems to be uncommon knowledge.
If SLAs are involved then I’d argue it’s not about optimization but about business goals, which unsurprisingly take precedence.
But there is another case that is very similar: threshold passing (or how I like to call it - waterfalling). Small inefficiencies add up and at some point a small slowdowns reach critical mass and some significant system breaks everything else.
When system was designed by competent engineers huge optimizations aren’t easy, so it’s shaving couple millis here and couple millis there. But, as in the first case, I’d categorize it as a performance failure.
Yeah, so the pathological incentives to build bad products are just insane. It’s more evidence against the whole market-Darwinian hypothesis being good for the consumer. “Fitness” doesn’t equal (let alone define) product quality.
That said, most developers (I assume, by most I mean myself) never work with code where the work they do has any serious impact on performance; it's layers on top of code that should be fast, but the work I do is implementing business logic and screens, in which case readability vastly trumps performance.
I mean right now I'm writing a line of code that can be represented as a nested ternary or a function call with some more written-out if/elses. The ternary outperforms the function call 2:1 or more, but either one can be executed hundreds of times a second BUT will only be executed once or twice per page view. It's not worth the tradeoff, even if this line of code will be executed hundreds of millions of times overall.
> You may make your function 2x faster, at a cost of additional complexity, but you never run it enough in a year for it to pay itself back.
I'm not talking about increased complexity, I'm talking about extremely basic things that take zero extra time, like using the correct data structure. For example, in Python:
In [8]: a = array("i", (x for x in range(1_000_000)))
...: l = [x for x in range(1_000_000)]
...: d = deque(l)
...: for x in (a, l, d):
...: print(f"{sys.getsizeof(x) / 2**20} MiB")
...:
3.902385711669922 MiB
8.057334899902344 MiB
7.868537902832031 MiB
Very similar structures, with very different memory requirements and access speeds. I can count on one hand with no fingers the number of times I've seen an array used.
Or knowing that `random.randint` is remarkably slow compared to `random.random()`, which can matter in a hot loop:
In [10]: %timeit math.floor(random.random() * 1_000_000)
31.9 ns ± 0.138 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
In [11]: %timeit random.randint(0, 1_000_000)
163 ns ± 0.653 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
> All of which will change in a few years, which is fine, if you're also committing to keeping _all that code_ up to date right along with it.
With the exception of list comprehension over large ranges slowing down from 3.11 --> now, I don't think there's been much in Python that's become dramatically worse such that you would need to refactor it later (I gather the Javascript community does this ritual every quarter or so). Anything being deprecated has years of warning.
This will need to happen 7.6 million times to save me 1 CPU second. On AWS lambda with 1GB of memory this will cost you a whopping: $0.0000166667.
The point is, you're not even wrong, but there are vanishingly few cases where it would actually matter to the bottom line in practice. You're taking an absolutist point of view to a discipline which thoroughly rejects it.
This is what I love about the cloud. It forces you to confront what your efforts are actually worth by placing a specific value on all of these commodities. In my experience they're often worth very little given that none of us have the scale of problems where this would show actual returns.
Reaching the scale where it shows actual returns isn't all that difficult. You need it to happen 7.6 million times to save 1 CPU second, but each CPU core can execute it nearly that many times every second.
Probably you don't leave it generating only random numbers all day, but suppose you do generate a good few, so that it's 1% of your total compute budget, and you have only a modest load, using on average four CPU cores at any given time. Then saving that amount of computation will have saved you something like $15/year in compute, recurring. Which isn't actually that bad a return for ten seconds worth of choosing the right function.
There are also a lot of even fairly small entities for which four cores is peanuts and they're running a hundred or a thousand at once, which quickly turns up the price.
And even the things with internet scale aren't all that rare. Suppose you're making a contribution to the mainline Linux kernel. It will run on billions of devices, possibly for decades. Even if it doesn't run very often, that's still a lot of cycles, and some of the kernel code does run very often. Likewise code in popular web browsers, javascript on popular websites or in popular libraries, etc.
You don't have to work for Google to make a contribution to zlib and that kind of stuff has the weight of the world on it.
Sure, but the cumulative effects of pervasive mediocre to bad decisions do add up. And it isn't just about cloud compute cost. Your own time is stolen by the slow ci jobs that you inevitably get stuck waiting for. For me, I prioritize my own personal
happiness in my work and this mindset taken too far makes me unhappy.
> Very similar structures, with very different memory requirements and access speeds. I can count on one hand with no fingers the number of times I've seen an array used.
That is obvious when you actually check the access speed of arrays and find out it is about half that of lists on small integers (under 256), and worse on non-small integers. That is literally the opposite trade off of what you want in 99.99% of cases.
Deques are even less of a consideration, they’re unrolled linked lists so random access is impossible and iteration is slower, you use a deque when you need a deque (or at least a fifo), aka when you need to routinely manipulate the head of the collection.
It depends on your constraints. If you’re limited by RAM, arrays make a lot of sense for certain applications. If you need Python’s buffer protocol, again, they make a lot of sense.
As to deques, yes, they have specific uses, and being slightly smaller isn’t usually a selling point for them. My point was that I have seen many cases where an incorrect data structure was used, because a list or dict was “good enough.” And sure, they generally are, but if the language ships with other options, why wouldn’t you explore those?
Yes but if you want to do things in a less obvious way you should be aware of the downsides, such as bias in your random numbers. Also making sure you watch out for off by one errors.
Stolen the number to show this off well from a bug report somewhere:
random_counter = Counter()
for i in range(10_000_000):
result = floor(random() * 6755399441055744) % 3
random_counter[result] += 1
print("floor method", random_counter.most_common(3))
randint_counter = Counter()
for i in range(10_000_000):
result = randint(0, 6755399441055743) % 3
randint_counter[result] += 1
print("randint method", randint_counter.most_common(3))
Python is 100% the wrong language to worry about this in. If your hot loops are in python and you care about performance, you should be rewriting them in another language.
Agreed; I used it partially because TFA used it to demonstrate ideas, and partially because I’m very familiar with it.
But you’re correct, of course. When I need something to go faster in Python, I write it in C. If it’s more than a small section, then a full rewrite is reasonable.
I was curious why randint was slower than random and found a good SO answer [0] (it's like chatgpt by humans for you youngsters out there), the gist of it is that `random()` calls a C function directly (which I presume goes straight to a syscall), whereas `randint` is implemented in Python and has a load of preconditions / defensive programming (which I presume is executed at every invocation, so seven potential branches etc it has to check every time).
Of course, if it's not in a tight loop and you need a whole integer between a range then it's the most developer-ergonomic way to get it. If you do need more performance but want to keep the ergonomics, writing your own random-int-between-two-numbers is fairly straightforward but it'll take some time.
Ok, but neither of these matter until you know they matter. Seriously. Like, yes, it's nice they exist and that they are available for when you want them, but I would generally advise people to use a list or random.randint, if only because I value their confidence with them over the 2x performance win, because most workloads are not simply just a single array or random number generator loop. And, to be clear, I work on performance professionally: most of my job is not making things as fast as possible, but considering the tradeoffs that go into writing that code. I understand your example as showing off an interesting performance story but in the real world most workloads are more complex than what can be solved with using a rare but drop-in API.
> until you get into the millions of records per second level, you're almost never benefited
Yeah, but the software landscape is very diverse.
On my job (CAM/CAE) I often handle data structures with gigabytes of data. Worse, unlike e.g. multimedia frameworks many algorithms operating on these numbers are global i.e. can’t be represented as a pipeline which splits data into independent chunks and processes them sequentially.
Making performance critical functions twice as fast might saves hours of time for a single end user in a single day.
> It also frustrates me to no end that people are so deeply incurious. This seems to only happen in tech,
I mean software engineering and computer science from the start is built on abstraction, it makes sense that it attracts people who find taking the abstraction as reality and ignoring the underlying "real" layers to be attractive.
(Also it's not "real" until you get down to... voltages? Electrons? I don't know... the whole thing about our whole endeavor being built of abstraction is there are so many layers. Nobody thinks everyone has to routinely go ALL THE WAY down, but I get your point that being more curious than many tend to be about a couple layers down is helpful, with how far down a couple layers is depending on the nature of your work. I'm not saying it's not helpful, I'm suggesting possibly why -- because we select for people who swim happily in abstractions, who love em even. And the success of all of CS and software engineering is based on the ability for it to often work. nobody really has to be curious about voltages when writing a python script to read and write from storage)
Nope. If a program is meant to run on the entire CPU, and if it's meant to use all the memory, then that's how it will run. This isn't a question of modernity or availability of tools or programmers' salary.
Or, to put it differently, using more or less memory / CPU isn't really an indication of anything about a program. Sometimes you mean to use all that's available, other times you mean to use as little as you can. There's no way to tell which one should be done w/o knowing your goals.
For example, the entire free memory on Linux is a free real estate for filesystem caching. So that memory is not wasted, if the system can help it. So, having your entire memory used (to support filesystem cache) wouldn't be really a performance problem (quite the contrary, it would look like the system is doing a good job at caching whatever reads your applications are making).
More importantly, this attitude is precisely why software sucks today. "[CPU, Memory, Disk] is cheap, engineering time isn't." Fuck that. Bad engineering time is _incredibly_ expensive. This is an excuse to not spend time learning the ins and outs of your language, and your hardware.
It also frustrates me to no end that people are so deeply incurious. This seems to only happen in tech, which is baffling considering how incredibly broad and deep the industry is. Instead, everyone clamors to learn the new Javascript abstraction that lets them get even further away from the reality of what the magic boxes are doing.